Learning Unsupervised Visual Grounding Through Semantic Self-Supervision
The Robotics Institute（机器人研究所）；Carnegie Mellon University（卡内基梅隆大学）；Kohli Center of Intelligent Systems (KCIS)（科利智能系统中心（KCIS））；IIIT-Hyderabad（海得拉巴国际信息技术学院）
Localizing natural language phrases in images is a challenging problem that requires joint understanding of both the textual and visual modalities. In the unsupervised setting, lack of supervisory signals exacerbate this difficulty. In this paper, we propose a novel framework for unsupervised visual grounding which uses concept learning as a proxy task to obtain self-supervision. The simple intuition behind this idea is to encourage the model to localize to regions which can explain some semantic property in the data, in our case, the property being the presence of a concept in a set of images. We present thorough quantitative and qualitative experiments to demonstrate the efficacy of our approach and show a 5.6% improvement over the current state of the art on Visual Genome dataset, a 5.8% improvement on the ReferItGame dataset and comparable to state-of-art performance on the Flickr30k dataset.
ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing Supervision
IBM Research Zurich, Zurich, Switzerland（IBM苏黎世研究实验室，瑞士苏黎世）；ETH Zurich, Computer Vision（苏黎世联邦理工学院，计算机视觉）；ETH AI Center, Switzerland（苏黎世联邦理工学院人工智能中心，瑞士）；ETH Zurich, Photogrammetry（苏黎世联邦理工学院，摄影测量学）；Microsoft, Switzerland（微软公司，瑞士）
Stanford University（斯坦福大学）；Universidad Catolica de Chile（智利天主教大学）
A key aspect of VQA models that are interpretable is their ability to ground their answers to relevant regions in the image. Current approaches with this capability rely on supervised learning and human annotated groundings to train attention mechanisms inside the VQA architecture. Unfortunately, obtaining human annotations specific for visual grounding is difficult and expensive. In this work, we demonstrate that we can effectively train a VQA architecture with grounding supervision that can be automatically obtained from available region descriptions and object annotations. We also show that…
DeepMind（深度思维）；Carnegie Mellon University（卡内基梅隆大学）；University of Oxford（牛津大学）
There are thousands of actively spoken languages on Earth, but a single visual world. Grounding in this visual world has the potential to bridge the gap between all these languages. Our goal is to use visual grounding to improve unsupervised word mapping between languages. The key idea is to establish a common visual representation between two languages by learning embeddings from unpaired instructional videos narrated in the native language. Given this shared embedding we demonstrate that (i) we can map words between the languages, particularly the 'visual' words; (ii) that the shared embeddi…
GeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision
Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences（中国科学院目标认知与应用技术重点实验室）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
Georgia Institute of Technology（佐治亚理工学院）；SRI International（SRI国际）；Facebook AI Research（Facebook人工智能研究院）
We address the problem of grounding free-form textual phrases by using weak supervision from image-caption pairs. We propose a novel end-to-end model that uses caption-to-image retrieval as a `downstream' task to guide the process of phrase localization. Our method, as a first step, infers the latent correspondences between regions-of-interest (RoIs) and phrases in the caption and creates a discriminative image representation using these matched RoIs. In a subsequent step, this (learned) representation is aligned with the caption. Our key contribution lies in building this `caption-conditioned…
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding
Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；Peng Cheng Laboratory (PCL)（鹏城实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
Nanyang Technological University（南洋理工大学）；Renmin University of China（中国人民大学）；Columbia University（哥伦比亚大学）
We focus on grounding (i.e., localizing or linking) referring expressions in images, e.g., "largest elephant standing behind baby elephant". This is a general yet challenging vision-language task since it does not only require the localization of objects, but also the multimodal comprehension of context --- visual attributes (e.g., "largest", "baby") and relationships (e.g., "behind") that help to distinguish the referent from other objects, especially those of the same category. Due to the exponential complexity involved in modeling the context associated with multiple image regions, existing…
Renmin University of China（中国人民大学）；Nanyang Technological University（南洋理工大学）；Columbia University（哥伦比亚大学）
We focus on grounding (i.e., localizing or linking) referring expressions in images, e.g., ``largest elephant standing behind baby elephant''. This is a general yet challenging vision-language task since it does not only require the localization of objects, but also the multimodal comprehension of context -- visual attributes (e.g., ``largest'', ``baby'') and relationships (e.g., ``behind'') that help to distinguish the referent from other objects, especially those of the same category. Due to the exponential complexity involved in modeling the context associated with multiple image regions, e…
Training a reinforcement learning agent to carry out natural language instructions is limited by the available supervision, i.e. knowing when the instruction has been carried out. We adapt the CLEVR visual question answering dataset to generate complex natural language navigation instructions and accompanying scene graphs, yielding an environment-agnostic supervised dataset. To demonstrate the use of this data set, we map the scenes to the VizDoom environment and use the architecture in \citet{gatedattention} to train an agent to carry out these more complex language instructions.
A-PAIR: A Benchmark and Identity-Consistent Grounding Framework for Air-Ground Cross-View Referring Person Detection
School of Automation, Southeast University（东南大学自动化学院）；Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Sports Training, Tianjin University of Sport（天津体育学院运动训练学院）；Tianjin University（天津大学）；National University of Defense Technology（国防科技大学）
Groc-PO: Grounded Context Preference Optimization for Truthful Multimodal LLMs
University of Science and Technology of China（中国科学技术大学）；Xiaomi Corporation（小米公司）；State Key Laboratory of Communication Content Cognition, People’s Daily Online（人民日报社传播内容认知国家重点实验室）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Textual grounding, i.e., linking words to objects in images, is a challenging but important task for robotics and human-computer interaction. Existing techniques benefit from recent progress in deep learning and generally formulate the task as a supervised learning problem, selecting a bounding box from a set of possible options. To train these deep net based approaches, access to a large-scale datasets is required, however, constructing such a dataset is time-consuming and expensive. Therefore, we develop a completely unsupervised mechanism for textual grounding using hypothesis testing as a …
Xidian University（西安电子科技大学）；Alibaba Group（阿里巴巴集团）；The Hong Kong University of Science and Technology（香港科技大学）；National University of Singapore（新加坡国立大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Current approaches for video grounding propose kinds of complex architectures to capture the video-text relations, and have achieved impressive improvements. However, it is hard to learn the complicated multi-modal relations by only architecture designing in fact. In this paper, we introduce a novel Support-set Based Cross-Supervision (Sscs) module which can improve existing methods during training phase without extra inference cost. The proposed Sscs module contains two main components, i.e., discriminative contrastive objective and generative caption objective. The contrastive objective aims…
Unsupervised learning of optical flow, which leverages the supervision from view synthesis, has emerged as a promising alternative to supervised methods. However, the objective of unsupervised learning is likely to be unreliable in challenging scenes. In this work, we present a framework to use more reliable supervision from transformations. It simply twists the general unsupervised learning pipeline by running another forward pass with transformed data from augmentation, along with using transformed predictions of original data as the self-supervision signal. Besides, we further introduce a l…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；UC Berkeley（加州大学伯克利分校）；ICSI（国际计算机科学研究所）
Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual content is a challenging problem with many applications for human-computer interaction and image-text reference resolution. Few datasets provide the ground truth spatial localization of phrases, thus it is desirable to learn from data with no or little grounding supervision. We propose a novel approach which learns grounding by reconstructing a given phrase using an attention mechanism, which can be either latent or optimized directly. During training our approach encodes the phrase using a recurrent network language mo…
Omnidirectional multi-view stereo (MVS) vision is attractive for its ultra-wide field-of-view (FoV), enabling machines to perceive 360° 3D surroundings. However, the existing solutions require expensive dense depth labels for supervision, making them impractical in real-world applications. In this paper, we propose the first unsupervised omnidirectional MVS framework based on multiple fisheye images. To this end, we project all images to a virtual view center and composite two panoramic images with spherical geometry from two pairs of back-to-back fisheye images. The two 360° images formulate …
Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding
Hubei Key Laboratory of Distributed System Security（湖北分布式系统安全重点实验室）；Hubei Engineering Research Center on Big Data Security（湖北大数据安全工程研究中心）；School of Cyber Science and Engineering（网络安全学院）；Huazhong University of Science and Technology（华中科技大学）；Peking University（北京大学）；Henan University（河南大学）；The Chinese University of Hong Kong（香港中文大学）；Guangzhou University（广州大学）；Protagolabs Inc.（Protagolabs公司）
What, when, and where? -- Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions
Columbia University（哥伦比亚大学）；Goethe University Frankfurt（法兰克福大学）；University of Bonn（波恩大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Quality Match GmbH（Quality Match有限公司）；IBM Research AI（IBM研究院人工智能部门）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
A steady momentum of innovations and breakthroughs has convincingly pushed the limits of unsupervised image representation learning. Compared to static 2D images, video has one more dimension (time). The inherent supervision existing in such sequential structure offers a fertile ground for building unsupervised learning models. In this paper, we compose a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives. We materialize the supervisory signals through determining whether a pair of samples is from one frame or from on…
In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation. There is a significant drop in performance of an existing segmentation model when training (source domain) and testing (target domain) data originate from different LiDAR sensors. To overcome this shortcoming, we propose an unsupervised domain adaptation framework that leverages unlabeled target domain data for self-supervision, coupled with an unpaired mask transfer strategy to mitigate the impact of domain shifts. Furthermore, we introduce the gated adapter …
AlphaRAD: Grounded Zero-Shot Classification in Chest Radiology via $α$-Corrected Binary Cross Entropy and Factorized Latent Supervision
Peter Munk Cardiac Centre, University Health Network (UHN)（彼得·芒克心脏中心，大学健康网络（UHN））；Ted Rogers Centre for Heart Research（泰德·罗杰斯心脏研究中心）；University of Toronto (U of T)（多伦多大学）；Vector Institute（向量研究所）；Toronto General Hospital Research Institute, UHN（多伦多总医院研究所，大学健康网络（UHN））
Multi-modal video question answering aims to predict correct answer and localize the temporal boundary relevant to the question. The temporal annotations of questions improve QA performance and interpretability of recent works, but they are usually empirical and costly. To avoid the temporal annotations, we devise a weakly supervised question grounding (WSQG) setting, where only QA annotations are used and the relevant temporal boundaries are generated according to the temporal attention scores. To substitute the temporal annotations, we transform the correspondence between frames and subtitle…
Huazhong University of Science and Technology（华中科技大学）；ProtagoLabs Inc（ProtagoLabs 公司）；Microsoft Research（微软研究院）；Dalian University of Technology（大连理工大学）
Temporal video grounding (TVG) aims to localize a target segment in a video according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on abundant video-query paired data, which is expensive and time-consuming to collect in real-world scenarios. In this paper, we explore whether a video grounding model can be learned without any paired annotations. To the best of our knowledge, this paper is the first work trying to address TVG in an unsupervised setting. Considering there is no paired supervision, we propose a novel Deep Semant…
We address the problem of discovering part segmentations of articulated objects without supervision. In contrast to keypoints, part segmentations provide information about part localizations on the level of individual pixels. Capturing both locations and semantics, they are an attractive target for supervised learning approaches. However, large annotation costs limit the scalability of supervised algorithms to other object categories than humans. Unsupervised approaches potentially allow to use much more data at a lower cost. Most existing unsupervised approaches focus on learning abstract rep…
Instance segmentation is an active topic in computer vision that is usually solved by using supervised learning approaches over very large datasets composed of object level masks. Obtaining such a dataset for any new domain can be very expensive and time-consuming. In addition, models trained on certain annotated categories do not generalize well to unseen objects. The goal of this paper is to propose a method that can perform unsupervised discovery of long-tail categories in instance segmentation, through learning instance embeddings of masked regions. Leveraging rich relationship and hierarc…
University of Technology Sydney（悉尼科技大学）；Facebook Reality Labs（脸书现实实验室）
In this paper, we present supervision-by-registration, an unsupervised approach to improve the precision of facial landmark detectors on both images and video. Our key observation is that the detections of the same landmark in adjacent frames should be coherent with registration, i.e., optical flow. Interestingly, the coherency of optical flow is a source of supervision that does not require manual labeling, and can be leveraged during detector training. For example, we can enforce in the training loss function that a detected landmark at frame$_{t-1}$ followed by optical flow tracking from fr…
Pseudo-labelling is a popular technique in unsuper-vised domain adaptation for semantic segmentation. However, pseudo labels are noisy and inevitably have confirmation bias due to the discrepancy between source and target domains and training process. In this paper, we train the model by the pseudo labels which are implicitly produced by itself to learn new complementary knowledge about target domain. Specifically, we propose a tri-learning architecture, where every two branches produce the pseudo labels to train the third one. And we align the pseudo labels based on the similarity of the prob…
Amazon Lab126（亚马逊Lab126）；Georgia Institute of Technology（佐治亚理工学院）
We present an unsupervised learning approach to recover 3D human pose from 2D skeletal joints extracted from a single image. Our method does not require any multi-view image data, 3D skeletons, correspondences between 2D-3D points, or use previously learned 3D priors during training. A lifting network accepts 2D landmarks as inputs and generates a corresponding 3D skeleton estimate. During training, the recovered 3D skeleton is reprojected on random camera viewpoints to generate new "synthetic" 2D poses. By lifting the synthetic 2D poses back to 3D and re-projecting them in the original camera…
LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Tongyi Lab, Alibaba Group（阿里巴巴集团通义实验室）；Peng Cheng Laboratory（鹏城实验室）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））
